Operating photovoltaic (PV) systems under partial shading conditions results in significant power losses. To mitigate partial shading effects, distributed maximum power point tracking (DMPPT) architectures have been proposed. An emerging DMPPT technique represented by PV module cascaded converters (MCCs) has been widely reported in the literature. In this architecture, a DC converter is allocated for each PV module to process and maximise its power. In this sense, mismatch effects are mitigated between PV modules. While MCC architecture has prominent advantages and value-added features, its challenges and limitations cannot be ignored. This study presents a comprehensive review of the state of the art of PV MCC architecture to help readers realise the progress of this DMPPT technique. Several points are extensively discussed and analysed including concept realisation and analysis, DC converter topologies and design optimisation, DMPPT performance limitations, DMPPT control, and protection. The main concepts are reemphasised through a set of simulations. Finally, a list of potential research areas in this field is introduced.

Wind power uncertainties have made the large integration of wind power generating units in the power system highly challenging. One promising solution to overcome the challenges associated with the intermittency of the renewable energy resources (RESs) is to connect areas with diverse renewable energy portfolios via high voltage direct current (HVDC) transmission lines with controllable power transfer capability. The installation of HVDC transmission lines in the power system has resulted in the evolution of conventional alternating current (AC) networks to mixed AC-HVDC power systems. In this study, to address wind power uncertainties in mixed AC-HVDC multi-area power systems, a modified robust optimisation (RO) model for the security-constrained economic dispatch (SCED) problem is proposed. The proposed RO model is used to minimise the generation cost and wind power curtailment under the worst-case scenario of actual wind power. Unlike the existing RO models, the proposed RO model considers a modified uncertainty set based on the wind power admissibility and addresses the budget of uncertainty more accurately to adjust the solution's level of conservatism. Extensive numerical studies demonstrate the economic and operational advantages of the proposed RO model for solving the SCED problem in mixed AC-HVDC power systems with high penetration of RESs.

In this study, the distribution effect of grid-connected inverters in photovoltaic (PV) plants is analysed. In fact, the interaction dynamics between different arrangements of distributed PV inverters and the grid are thoroughly compared. To this aim, the total power of all arrangements of distributed inverters is considered constant, which is called fixed-power arrangements in this work. Accordingly, this study first develops the model of grid-connected current-controlled inverters (CCIs). Then, by applying the impedance-based criterion, the effect of CCIs distribution on their stability is evaluated. Despite the fact that each CCI model is obtained based on the same stability margins, different distributed arrangements of CCIs have different interaction dynamics with the grid. From the frequency-domain analysis, it is found that the distribution effect of CCIs can greatly affect the high-frequency (HF) behaviour of their output admittances, and consequently improves their interaction dynamics; hence, one of the factors which can also play an important role in HF behaviour is distribution effect of CCIs in a PV plant. The simulation studies of several distributed arrangements of CCIs as well as a centralised CCI confirm the validity of the performed analysis.

This study is interested in optimisation of both sizing and energy management system of a hybrid storage system (HSS) associated with photovoltaic panels. The battery (BT) considered as the principal storage organ and a super-capacitor used as the secondary storage system to improve the BT life span makes up the HSS. The main purpose of this study is to explore a novel optimisation approach to jointly optimise the sizing and the fuzzy logic energy management system (FLEMS). In fact, an optimisation function based on sequential quadratic programming algorithm is proposed. The optimisation methodology has been performed jointly and successfully for the sizing of the BT storage system and the membership functions parameters of the FLEMS in order to decrease the levelised cost of energy with a violation time by 5% of mean absolute percentage error score <1.5% throughout the year. According to the simulations results, a benefit analysis has been done to assess the associated financial impact.

The phenomenon of partial shading adversely affects photovoltaic arrays, e.g. multiple peak points in – characteristic curves and reducing the output power. Array reconfiguration is the best and efficient way of alleviating the partial shading effects. Physical relocation of panels can be used as a practical solution to prevent shading problems by altering the physical location of panels by not changing its electrical circuit. In this study, a new reconfiguration technique named as Arrow Sudoku pattern is proposed for all the conventional configurations such as, series–parallel, bridge-link, honeycomb, and total cross-tied connections to mitigate the shading effects. The proposed approach is tested on a Solar Photo-Voltaic (SPV) array for different groups of shading conditions. The results are simulated through MATLAB/Simulink and validated through hardware prototype using artificial shade blockers and the results show a good improvement in the diagnosis of output parameters for all the reconfiguration techniques in comparison with their initial configurations. Moreover, the disturbance caused in – characteristic curves by shading is eradicated by using this proposed technique.

Flapping wings are known to be used not only for propulsion, but also for energy extraction from the surrounding fluid environment. In this work, the authors propose a vertical axis turbine based on flapping wings. Numerical experiments have been conducted. Results show (i) In a certain range of frequency, the vertical axis turbine equipped with flapping wings of circular or reversed-D motion trajectory demonstrates better energy extraction performance when compared to the traditional vertical axis wind turbine. (ii) For the reversed-D motion trajectory, as two airfoils begin to move apart from each other, the downstream flapping-wing takes advantage of the trailing edge vortex of the upstream flapping-wing to enhance its own energy extraction ability. However, for the circular motion trajectory, the two airfoils are usually located far away from each other. This leads to the result that the mutual influence between the two airfoils is very weak. Generally speaking, the application of flapping wings in the vertical axis turbine can improve the energy extraction efficiency in a certain range, which can provide another method for the design of a new type of vertical axis turbine in the research and industry.

This study proposes an approach to help the bidding processes of hiring wind-photovoltaic farms in long-term energy auctions. The proposed approach aims to define an optimal solution to configure wind-photovoltaic farms based on mixture design of experiments and the L_p method, as well as an efficiency metric designed to achieve diversification and to identify the Pareto dominant optimal portfolio. The proposed method is simple and flexible for practical applications. Moreover, its associated goals of choosing the Pareto dominant optimal solutions are aligned with the goals of the electricity regulators responsible to manage the hiring process for a new generation. To validate the method, wind-solar photovoltaic generation configurations in three Brazilian cities are analysed and the results are compared with other methods previously proposed in the literature. The results show that the proposed method has more intuitive criteria for the investor and regulator, without reducing the quality of the information provided to decision making.

This study reports on the development and implementation of the fuzzy incremental controller on a small-scale linear fresnel reflector solar plant. The control problem is concerned with forcing the output temperature to follow the reference, despite the existence of disturbances. Owing to the non-linear effects of solar plants, the capability of fixed parameters of the proportional–integral controller to cope with the control problem is limited and leads to unsatisfactory control performance. The proposed controller is a modification to a conventional proportional–integral algorithm with better tuning flexibility. Firstly, the ant colony optimisation algorithm is used to define the optimal parameter of proportional–integral plus series feed-forward controller while guaranteeing a satisfactory performance of the plant. Secondly, the resulting controller is replaced with an equivalent fuzzy knowledge-based controller with the error and change of error of the plant as the input variables and flow rate as an output variable. The proposed controller is tested on a quasi-dynamic model of linear fresnel reflector plant using conventional and renewable energy optimisation toolbox in the environment of MATLAB/Simulink. The controller's performance is compared to the proportional–integral controller, where its effectiveness is evaluated under nominal conditions, measurement delay, noise, and presence of disturbance.

Distributed generation (DG) has rapidly increased due to many technical, environmental and economical benefits. One of the DG application challenges is to find a proper area to incorporate the DG associated to a particular location. In this study, a central photovoltaic distributed generation (PVDG) topology is proposed to distribute the optimal sizes to the optimal locations. Uncertainties of load demand and renewable power generation are also taken into consideration of the optimisation problem. This study determines the deterministic and probabilistic penetration limits based on the distribution network topologies, considering the PVDG significant impact on active power losses reduction and voltage profiles improvement. The effectiveness of the proposed topology was validated on 33- and 69-bus distribution networks adopting Monte Carlo simulation method, Newton-Raphson load flow method and biogeography based optimisation. From the results, the voltage profiles, active power loss reduction, DG capacity required and penetration limit have shown better performances on the central PVDG topology over the bus dedicated PVDG topology.

This study aims to design a stochastic distributed energy management framework taking into account the information privacy of autonomous agents. To develop this framework, first, the application of a scenario-based method on the predicted probability density function (PDF) is suggested to deal with uncertainties of the low-scale loads, renewable generations, i.e. wind and photovoltaic generations, and electricity market prices. Then, an alternative direction method of multipliers (ADMM) based method, namely over-relaxed ADMM, is presented to optimise the operational set points considering each agent benefits and technical constraints. In this framework, the agents participate in scheduling programs without sharing influential information and corresponding historical data. The presented framework is tested on a realistic small-scale microgrid (MG) system and real historical data. The performance and efficiency are verified by comparison of the proposed over-relaxed ADMM method application with the application of standard ADMM and analytic targeting cascading in terms of accuracy and convergence speed. Furthermore, higher accuracy and lower computational complexity of predictive PDF-based scenario generation techniques in distributed MG energy management are verified by comparison with distributed energy management based on predefined PDF and historical data.

The islanded hybrid AC/DC microgrid consisting of battery energy storage (BES) systems, photovoltaic (PV) generators, and bidirectional power converter (BPC) possesses the advantages of flexibility and extendibility. To ensure the safety and stability of operation, the power of BES and BPC needs to be restrained within their designed limits regardless of the power fluctuation in the hybrid microgrid. Besides, the over-charge and over-discharge of BES should be prohibited. In this study, a decentralised generation-storage-subgrid coordination control for power management is proposed to assure the power limitation and state of charge (SOC) protection. In the control strategy of BES, a modified droop method is adopted to deliver the storage's both SOC and output power signals without communication links. Meanwhile, a fuzzy logic controller is adopted in BPC, which can prevent the overuse of BESs in both subgrids and limit the maximum output power of BPC. In addition, a modified P&O strategy is applied in PV systems as a supplement of overuse protection. Finally, the effectiveness of the proposed control strategy is verified on RT-Lab experimental platform.

Estimating prediction intervals (PIs) is an efficient and reliable way of capturing the uncertainties associated with wind power forecasting. In this study, a state of the art recurrent neural network (RNN) known as long short-term memory (LSTM) is used to produce reliable PIs for one-hour ahead wind power uncertainty forecast using the non-parametric lower upper bound estimation framework. Two realistic hourly stamped wind power data sets are obtained and by using mutual information and false nearest neighbours techniques, the data are made suitable for model inputs. A novel comprehensive objective function consisting of the coverage probability, the average width of the PIs, symmetricity and variational synchronicity is developed to train the LSTM model using intelligent optimisation techniques. The standard of the PIs generated for the test set as well as for different seasons are evaluated based on the indices used to design the objective function for model training, with one of them being modified. The performance of the proposed LSTM model is found to outperform typical RNN models like Elman, non-linear auto-regressive with exogenous models and other benchmarking models while tested on the real-world data sets.

DC microgrids are well known as a proper solution to link different DC sources, such as photovoltaic panels and wind turbines, directly to DC loads. Along with their advantages, they suffer from an imbalance state of charge (SOC) in their energy storage units (ESUs), improper current-sharing between ESUs, and DC bus voltage deviation. This study proposes a novel control strategy for DC microgrids, which not only balances ESUs SOC and shares current between ESUs proportional to their capacity but also, reduces DC bus voltage deviation significantly. In this way, the same droop curve is defined. Then, three virtual resistances are considered. The first one is set based on the lines resistance and ESUs capacity to share current among the ESUs properly. As the lines resistance changes affect the proportional current-sharing, the second virtual resistance is intended to relieve this effect. The third one is suggested to balance the ESUs SOC. The small-signal stability analysis is performed, which demonstrates that the proposed method has stability in all operating conditions. Simulation results in MATLAB/Simulink confirm that with the aid of the proposed control method, not only proper current-sharing and ESUs SOC balancing are obtained, but also DC bus voltage deviation is effectively diminished.

This study presents a multi-objective based on a hybrid methodology to apply the problem of economic emission dispatch that is incorporated with hydro, thermal and wind power units. The hybrid method is the combination of both the modified quantum-behaviour lightning search algorithm (MQLSA) and artificial intelligence technique with the help of PSNN. To determine the optimal amounts of the generated powers from the thermal, wind farms and hydro units, the proposed approach is introduced by minimising the cost of generation and the emission level simultaneously. MQLSA is utilised to generate the optimal combination of thermal power with the objective of the minimum error function to minimise the fuel and emission cost of the system with wind speed factor. To capture the optimised wind power with the objective of minimum speed factor, the PSO-artificial neural network technique is utilised. The algorithm is integrated with the feasible solution constraint handling techniques. To validate the effectiveness of the proposed method, the six and ten generating thermal system with wind power is studied. The proposed hybrid technique is implemented in MATLAB/Simulink platform and the simulation result demonstrates the superiority of the proposed method compared to the various existing techniques.

Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present study is carried out for automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction-based support vector machine (SVM) and convolutional neural network (CNN) are used for the solar cell defect classifications. Suitable hyperparameters, algorithm optimisers, and loss functions are used to achieve the best performance. Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature Transform (SIFT) and speeded-up-robust features (SURF) are used to train the SVM classifier. Finally, the performance results are compared. It is concluded that CNN's accuracy for solar cell defect classification is 91.58% which outperforms the state-of-the-art methods. With features extraction-based SVM, accuracies of 69.95, 71.04, 68.90, and 72.74% are obtained for HOG, KAZE, SIFT, and SURF, respectively. The present study may contribute to making a PV system more efficient for classifying defects to improve the power system efficiency.

Snake-like wave energy conversion (WEC) is a new type of WEC that combines raft WEC and magnetoelectric transducer. This type of WEC uses the magnetoelectric transducer, which is designed based on the permanent magnet linear generator and Halbach permanent magnet array, as its power take-off unit. The purpose of this study is to design a matching magnetoelectric transducer for WEC in common wave conditions and test its performance. The hydrodynamic model of the snake-like WEC is established to study the motion of WEC in regular waves. The equivalent magnetic circuit model of the magnetoelectric transducer is set up to optimise its ability to harvest wave energy in the ocean. Taking the maximum damping coefficient (c_{m ·max}) as the optimisation goal of the magnetoelectric transducer, the induced voltage and output power of the magnetoelectric transducer in relatively uniform and sinusoidal motion can be calculated by simulation software. Finally, a prototype of the magnetoelectric transducer was made and its performance was tested. The experiment shows that the magnetoelectric transducer has a good ability to harvest wave energy, and snake-like WEC has wide applicability.

Nowadays, an increasing number of photovoltaic (PV) plants are becoming integrated into one regional power grid. Under this circumstance, the probabilistic forecast of regional PV power generation is of significance for the regional power system operation and control. This study presents a novel probabilistic forecast method for regional PV generation that integrates the convolutional neural network (CNN) with non-linear quantile regression (QR). In this method, the CNN structure is enhanced to extract the non-linear features of the input data and generate the non-linear QR function. As a result, the improved CNN can effectively process high-dimensional and complex input data and the non-linear QR model can provide quantile forecast information of regional PV power. The validity of the proposed method is verified by using it to forecast the regional PV generation from the clustered PV plants in the Weifang region of China.

Due to the progressive penetration and usage of renewable sources and loads based on power electronics, medium voltage direct current (MVDC) distribution system is getting broad attention. Direct current circuit breakers (DCCBs) are of vital importance for the reliability and flexibility of power system. With features of low cost and micro-operating losses, multi-port hybrid DCCB with negative voltage source (NVS) has been proposed by the authors and might be a better choice. To further promote its industry application in MVDC system, interaction characteristics between DCCB and power system are investigated in this study. The structure of multi-port hybrid DCCB is briefly introduced. Then, considering the diversified working conditions, e.g. single fault, multiple faults and switching load current with random direction, the cooperation sequence of components in multi-port DCCB under all these working conditions is proposed, respectively. Then, based on simulation model established in PSCAD/EMTDC, transient current/voltage distribution pattern inside multi-port DCCB and its mechanism are discussed, and simulation results have verified the superiority and effectiveness of multi-port hybrid DCCB with NVS in MVDC system.

The increasing penetration of photovoltaic (PV) energy in power grids will impose system instability issues, especially in the occurrence of faults. However, very limited research has been conducted on the low-voltage ride-through (LVRT) control of PV systems in the low-voltage distribution networks (LVDNs) with predominantly resistive line impedances. To fulfil this remaining gap, the effects of active current injection (ACI) on the grid voltage support in LVDN is mathematically analysed in this study. Subsequently, a novel LVRT control scheme for the PV grid-forming inverter is proposed, where the control distinguishes itself from other existing methods due to its optimisation of ACI and PV energy harvesting with the premise of system safety and specified reactive current injection as per grid codes. Multi-mode control modes are involved in the proposed method for dealing with different environmental conditions and voltage dips. Meanwhile, the DC-link voltage is adaptively operated in a self-adjustable control structure for improving grid resilience. The effectiveness of the proposed control method is verified by simulations in MATALB Simulink and hardware experiments on a PV microinverter. Compared with the traditional LVRT control, the post-fault power recovery and voltage support capability can be significantly improved.

Zero-voltage ride through (ZVRT) is the extreme case of a low-voltage ride through (LVRT), which represents the optimal grid-connection capability of a wind turbine (WT). Enforcing ZVRT will improve the dynamic performance of a WT under the extreme conditions, and therefore significantly enhance the resiliency of a renewable-rich power grid. Compared with LVRT, there are few research on ZVRT with no field validation been conducted. The feasibility of the ZVRT requirement, especially the 400-ms-level fault-time condition, is not clear yet. The first system-level study including the control strategy, transient analysis, hardware enhancement strategy, WT modelling and field test on the ZVRT capability of a Type-3 WT is presented in this study. Particularly, to meet the challenging technical requirements, the authors proposed a hardware enhancement strategy, which accommodates with the widely-used LVRT control approach. The holistic strategy is tested under the extreme grid-connection condition by both simulation and a costly field test. The field test in the 400-ms-level fault-time condition based on an operating WT in a weak power system directly verifies the feasibility and effectiveness of ZVRT for a Type-3 WT. Furthermore, the critical time window and the key parameter ranges during ZVRT, which significantly affect ZVRT performance, are obtained.

Cyber security of microgrids attracts increasing attention since the relatively open communication network of the microgrids is vulnerable to hacker attacks. This study proposes a real-time distributed economic dispatch scheme for the grid-connected microgrid against the cyberattacks. Firstly, a virtual leader agent is placed at the point of common coupling to measure the real-time power tracking error for achieving the power supply–demand balance, and then the optimal solution of the economic dispatch model is solved by the consensus algorithm of the multi-agent system theory. Subsequently, a set of detection algorithms is designed for each generator to determine whether its neighbouring generators suffer from the cyberattacks. The attacked generator is gradually marginalised from the communication network through updating the weight in the communication adjacency matrix based on its reputation value, thereby the impact of the attacked generator on the whole system is reduced greatly. Finally, the numerical simulations are carried out to verify the effectiveness of the proposed cyberattack-resilient distributed economic dispatch scheme.

The accumulation of dust on any given photovoltaic (PV) module surface depends on the type of dust, environment, surroundings, weather, module properties, and its installation design. In this research, equations were developed for the preliminary evaluation and comparison of the reduction in power from PV modules because of dust soiling. Use of these equations shows that dust accumulation decreases solar irradiance and thus the power output of modules, and that there is a linear relationship between the power output degradation and density or amount of accumulated dust. The equations can be used to conduct analysis of average photon energy and the PV module power output reduction from accumulated dust. The study also showed that type of PV module can also affect the degree of power output reduction. The amorphous silicon PV modules are more affected compared to poly crystalline silicon PV modules as the latter has a spectrum response which still has the range that can produce full energy and therefore, dust soiling has lesser impact on them. PV power plants should regularly clean the modules, if not, production of electricity decreases, and so too the revenue from selling electricity, making the payback of the power plant longer.

Renewable energy is the eventual objective for alleviating greenhouse gas (GHG) emissions and for energy affordability and reliability. This study addresses these issues in Jordan, as an oil-importing developing country. This is done by investigating an optimal sizing methodology for a hybrid photovoltaic (PV) biogas energy system in Ramtha, Jordan in case of on-grid and off-grid system's configurations. The multi-objective grey wolf optimisation algorithm is used to get non-dominant solutions of loss of power supply probability (LPSP) and total current cost (TCC) in a case and GHG emissions with TCC in another case. Hourly measured real values of solar irradiance, temperature, municipal solid wastes and load demand are obtained from formal institutions in Jordan. Detailed mathematical modelling is performed for the proposed system to precisely evaluate its performance. Non-dominant Pareto points are discussed in each Pareto front. These include affordable, compromised, and reliable or environmental Pareto points. The on-grid system is found to be more reliable and cost-effective than the off-grid PV biogas energy system. Further, the compromised solution of the on-grid system is found to be environmentally friendly. Finally, uncertainty investigation is conducted to examine the validity and test strength of the system's design.